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Activity Number:
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639
- Influential Observations: Detection and Modeling
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Type:
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Invited
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Date/Time:
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Thursday, August 3, 2017 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract #322153
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View Presentation
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Title:
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Reconciling Curvature and Importance Sampling Based Procedures for Summarizing Case Influence in Bayesian Models
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Author(s):
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Zachary Micah Thomas* and Mario Peruggia and Steven MacEachern
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Companies:
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Eli Lilly and Company and The Ohio State University and The Ohio State University
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Keywords:
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Bayesian ;
Case-Influence ;
Model Diagnostics ;
Kullback-Leibler ;
Outliers ;
Hierarchical Model
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Abstract:
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Methods for summarizing case influence in Bayesian models take essentially two forms: (1) use common divergence measures for calculating distances between the full-data posterior and the case-deleted posterior, and (2) measure the impact of infinitesimal perturbations to the likelihood to gain information about local case influence. Methods based on approach (1) lead naturally to considering the behavior of case-deletion importance sampling weights (the weights used to approximate samples from the case-deleted posterior using samples from the full posterior). Methods based on approach (2) lead naturally to considering the local curvature of the Kullback-Leibler divergence of the full posterior from a geometrically perturbed quasi-posterior. By examining the connections between the two approaches, we establish a rationale for employing low-dimensional summaries of case influence that are obtained entirely via the variance-covariance matrix of the log importance sampling weights.
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Authors who are presenting talks have a * after their name.
